Advances in the biological sciences and technology are providing molecular targets with clinical utility for diagnosing and treating breast cancer. Current classifications in pathology for the diagnosis of breast cancer are based primarily on histopathological features and anatomic staging (i.e., tumor-node-metastasis). These criteria have prognostic significance but provide little information for guiding therapy. The only accepted molecular markers that predict response to therapy in breast cancer are the hormonal receptors for estrogen (ER) and progesterone (PgR). Other markers, such as HER-2/neu (erbB-2) and p53, continue to be studied for their predictive value. Recently, cDNA microarrays have shown that invasive breast carcinomas can be stratified based upon gene expression patterns and that these molecular "profiles" determine outcome. This molecular taxonomy agrees with the rudimentary classifications already used in pathology (e.g., ER and HER-2 status) and may provide additional information that can account for the observed phenotypic variation in clinical behavior. Our goals are to develop and implement molecular diagnostic tests for breast cancer. The incorporation of molecular targets into medicine for clinical trials and routine testing requires the development of assays that are amenable to the clinical laboratory. These assays need to be robust, rapid and cost-effective. Factors that facilitate finding statistical significance with many new markers include reaching an adequate study size and standardizing sample preparation, reagents and scoring criteria. We will use reverse transcription coupled to PCR (RT-PCR) to recapitulate microarray classifications using a selected minimal gene set. In addition, sequence-based methods and immunohistochemistry (IHC) will be used to assess p53 mutation status. Finally, a prospective study on 350 patients with breast cancer will be conducted. Tumors will be compared by cDNA microarray, real-lime quantitative RT-PCR and p53 status. Patients will be given the standard of care for treatment and clinical parameters will be correlated to molecular data. Multivariate analyses will be used to show statistical significance of molecular classifications. This study should provide a framework for molecular diagnostic testing of solid tumors in the clinical laboratory.